In current scenario, there are various techniques used for recognizing data such as text, objects in the images desired for a specific application. For example, in the case of automated bill processing systems, data such as bill details shall be recognized and extracted from the images of the bill. The images of the bill will have varying background such as white or color depending on the type of bill and the vendor. Conventional image processing techniques employ one type of approach for extracting the data from images. However, there may be multiple background variations in an image such that a single approach may not be sufficient for reading these varying background format images as each approach may be suitable for a particular background variation. For example, one approach using Open Computer Vision (OpenCV) is suitable for recognizing objects in white background images and another approach using Convolutional Neural Network (CNN) is suitable for images with colored background. Conventional techniques, fail to utilize multiple approaches for images to produce an overall accurate result under a given scenario. Further, conventional techniques fail to accurately determine data in images where there may be multiple background variations in a single image.